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Linear regression is the most basic and commonly used predictive analysis. Regression estimates are used to describe data and to explain the relationship between one dependent variable and one or more independent variables.
In summary, when there are more than two covariates in a regression model, correlations amongst covariates are informative but should not be the only criterion used to judge whether or not multicollinearity is a problem. Other diagnostic tools, such as the VIF and condition index should also be used and reported.